







Progress as a non-stationary argument

Spirits and the incompleteness of physics
Complexity, renormalization, and the spirits beyond the horizon of theory

Implementation Divergences · Issue #12 · DavidBuchanan314/millipds
The concrete behaviour of this implementation may diverge from that of the reference impl (that lives in https://github.com/bluesky-social/atproto), sometimes on purpose, sometimes accidentally, so...
Exponential Economist Meets Finite Physicist | Do the Math
[An updated treatment of some of this material appears in Chapter 2 of the Energy and Human Ambitions on a Finite Planet (free) textbook, also mirrors a 2022 article in Nature Physics..]
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both per-deletion run-time and steady-state error that do not grow with the length of the update sequence. We also introduce several new conceptual distinctions: for example, we can ask that after a deletion, the entire state maintained by the optimization algorithm is statistically indistinguishable from the state that would have resulted had we retrained, or we can ask for the weaker condition that only the observable output is statistically indistinguishable from the observable output that would have resulted from retraining. We are able to give more efficient deletion algorithms under this weaker deletion criterion.
The Next Great Divergence: How AI could split the world again if we don’t intervene | Brookings
Michael Muthukrishna and Philip Schellekens argue that AI, like past general-purpose technologies, could drive a new global divergence unless deliberate action ensures its benefits are broadly shared rather than geographically concentrated.

Ironwood: The first Google TPU for the age of inference
We’re introducing Ironwood, our seventh-generation Tensor Processing Unit (TPU) designed to power the age of generative AI inference.

The Jevons Paradox of AI - Wesley's notes
Why AI can make us more productive but will never save us time
The AI Revolution in Math Has Arrived | Quanta Magazine
AI is being used to prove new results at a rapid pace. Mathematicians think this is just the beginning.

The Kochen-Specker Theorem
The Kochen-Specker theorem is an important and subtle topic in the foundations of quantum mechanics (QM). The theorem demonstrates the impossibility of a certain type of interpretation of QM in terms of hidden variables (HV) that naturally suggests itself when one begins to consider the project of interpretating QM.We here present the theorem/argument and the foundational discussion surrounding it at different levels. The reader looking for a quick overview should read the following sections and subsections: 1, 2, 3.1, 3.2, 4, and 6. Those who read the whole entry will find proofs of some non-trivial claims in supplementary documents.
Mathematicians are grappling with the possibility that AI might eclipse them
I talked to 20 mathematicians about rapid AI progress in their field.

Leshem (Legend) Choshen 🤖🤗 @ACL @ICML on Twitter / X
I agree, what I say is that you talk about how much you lose of the gradient you compute, and I say, but if you just compute less, the result would lose less and still be equivalent or worse probably?More steps=more information that's true, but a bit of a separate battle right?— Leshem (Legend) Choshen 🤖🤗 @ACL @ICML (@LChoshen) August 17, 2026

Accelerating Science with Human+AI Review
This issue of NEJM AI features the first two articles published through our accelerated human+AI review process. In this editorial, we describe the invitation-only “Fast Track” process used to revi...

WESTENBERG | Substack
Field Notes on the Machine Age. Click to read WESTENBERG, by JA Westenberg, a Substack publication with tens of thousands of subscribers.

The fall of the theorem economy
How AI could destroy mathematics and barely touch it
